云制造环境下的演化博弈公差分配方法

李海虹; 孙红; 陈志恢; 刘志奇

doi:10.7652/xjtuxb202108021

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云制造环境下的演化博弈公差分配方法

更新时间：2025-07-09

- 云制造环境下的演化博弈公差分配方法
- A Tolerance Allocation Method Based on Evolutionary Game in Cloud Manufacturing
- 西安交通大学学报 2021年55卷第8期页码：175-182
- 作者机构：
  
  太原科技大学机械工程学院,太原,030024
- 作者简介：
- 基金信息：
  
  : 2020-12-30。作者简介: 李海虹(1978—),女,教授。基金项目: 国家自然科学基金资助项目(51975396);山西省留学回国人员科技活动择优资助项目(HGKY2019087)
- DOI：10.7652/xjtuxb202108021
  中图分类号： TH124
- 网络首发：2021-08-10，
  
  纸质出版：2021
- 稿件说明：
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李海虹, 孙红, 陈志恢, 等. 云制造环境下的演化博弈公差分配方法[J]. 西安交通大学学报, 2021,55(8):175-182.

A Tolerance Allocation Method Based on Evolutionary Game in Cloud Manufacturing[J]. 2021, 55(8): 175-182.
李海虹, 孙红, 陈志恢, 等. 云制造环境下的演化博弈公差分配方法[J]. 西安交通大学学报, 2021,55(8):175-182. DOI： 10.7652/xjtuxb202108021.

A Tolerance Allocation Method Based on Evolutionary Game in Cloud Manufacturing[J]. 2021, 55(8): 175-182. DOI： 10.7652/xjtuxb202108021.

摘要

针对云制造环境下复杂多样的制造资源对产品装配尺寸链公差分配的影响

提出了一种基于演化博弈的公差分配方法。首先以同属一个尺寸链中的各公差单元为博弈方

综合考虑公差设计方案的质量损失和资源价格两方面

将装配质量要求和成本转换为公差单元选择制造资源后的总支付

以公差单元对资源的选择为博弈策略

建立演化博弈模型; 然后

构造模型的复制动力学方程

模拟不同用户的策略适应过程; 最后

运用Lyapunov第二方法证明该模型能够收敛至演化均衡

并利用分布式迭代算法进行求解。以车身前端装配总成公差分配设计为例

验证该模型及算法的可行性与有效性

实验结果表明:质量损失系数对资源选择策略无影响

但会影响总支付; 所提演化博弈方法与非合作博弈方法、线性加权方法相比

可降低公差分配方案的质量损失及总支付

平均降低了28.3%～54.0%和18.2%～63.6%。将该演化博弈方法应用于装配尺寸链公差分配设计

实现了云制造环境下依据公差设计进行制造资源的选择。

Abstract

A tolerance allocation method based on evolutionary game is proposed to solve the influence of complex and diverse manufacturing resources on the tolerance allocation of product assembly dimension chain in cloud manufacturing(CMfg). Firstly

all tolerance elements in the same dimension chain are taken as the game parties

and an evolutionary game model is established with the selection of manufacturing resources. The game strategy comprehensively considers the quality loss and the resource price of the tolerance design scheme

and the assembly quality requirements and cost are converted into the total payment benefit after the selection of manufacturing resources by the tolerance element. Secondly

a replication dynamics equation of the model is constructed to simulate the strategy adaptation process of different users. Finally

the Lyapunov's second method is used to prove the convergence of the model to evolutionary equilibrium

and the distributed iterative algorithm is used to find its solution. The feasibility and effectiveness of the model and algorithm are verified by a tolerance design example of the front end assembly. Experimental results show that the quality loss coefficient has no effect on the resource selection strategy

but affects the total payment. Comparisons with the non-cooperative game method and the linear weighting method show that the proposed evolutionary game method reduces the quality loss and total payment of the tolerance allocation scheme by 28.3% to 54.0% and 18.2% to 63.6% on average

respectively. The proposed method can be applied to the tolerance allocation design of assembly dimensional chain and to realize the selection of manufacturing resources according to tolerance design in cloud manufacturing environment.

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references

郑丞, 金隼, 来新民, 等. 基于非合作博弈的公差分配优化 [J]. 机械工程学报, 2009, 45(10): 159-165.

ZHENG Cheng, JIN Sun, LAI Xinmin, et al. Tolerance allocation optimization based on Non-cooperative game analysis [J]. Journal of Mechanical Engineering, 2009, 45(10): 159-165.

王晓慧, 郭士意, 车冬冬, 等. 基于集合概念的虚公差理论与应用 [J]. 机械工程学报, 2019, 55(7): 172-177.

WANG Xiaohui, GUO Shiyi, CHE Dongdong, et al. Theory and application of virtual tolerance based on set concept [J]. Journal of Mechanical Engineering, 2019, 55(7): 172-177.

胡西彪, 张卫, 陆宝春, 等. 基于离散化成本-公差模型的多目标公差优化设计 [J]. 计算机集成制造系统, 2019, 25(1): 182-189.

HU Xibiao, ZHANG Wei, LU Baochun, et al. Multi-objective optimization design of tolerance based on discretized cost-tolerance model [J]. Computer Integrated Manufacturing Systems, 2019, 25(1): 182-189.

VIGNESH D, RAVINDRAN D. Tolerance allocation of complex assembly with nominal dimension selection using Artificial Bee Colony algorithm [J]. Proceedings of the Institution of Mechanical Engineers: Part C Journal of Mechanical Engineering Science, 2019, 233(1): 18-38.

施祥玲, 徐小明, 苏林林, 等. 基于NSGA-Ⅱ算法的航天产品装配公差多目标优化 [J]. 上海航天, 2020, 37(3): 121-125+146.

SHI Xiangling, XU Xiaoming, SU Linlin, et al. Multi-objective optimization based on NSGA-Ⅱ algorithm for assembly tolerance of aerospace products [J]. Aerospace Shanghai, 2020, 37(3): 121-125+146.

张子豪, 郭俊康, 洪军, 等. 航空发动机高压转子装配偏心预测和相位优化的智能算法应用研究 [J]. 西安交通大学学报, 2021, 55(2): 47-54.

ZHANG Zihao, GUO Junkang, HONG Jun, et al. Application study of intelligent algorithms for prediction and phase optimization of assembly eccentricity of aero-engine high pressure rotor [J]. Journal of Xi'an Jiaotong University, 2021, 55(2): 47-54.

栾新慧, 刘惠国, 刘银华. 基于装配工艺仿真的车门公差分配优化方法研究 [J]. 农业装备与车辆工程, 2018, 56(2): 12-15.

LUAN Xinhui, LIU Huiguo, LIU Yinhua. Research on optimization method of car door tolerance allocation based on assembly process simulation [J]. Agricultural Equipment Vehicle Engineering, 2018, 56(2): 12-15.

KUMAR L, PADMANABAN K, BALAMURUGAN C. Optimal tolerance allocation in a complex assembly using evolutionary algorithms [J]. International Journal of Simulation Modelling, 2016, 15(1): 121-132.

曲兴田, 张昆, 王学旭, 等. 基于混合循环算法的复杂装配体装配序列智能规划 [J]. 东北大学学报, 2019, 40(12): 1767-1772.

QU Xingtian, ZHANG Kun, WANG Xuexu, et al. Hybrid cycle algorithm-based Intelligent assembly sequence planning of complex assembly [J]. Journal of Northeastern University, 2019, 40(12): 1767-1772.

HAGHIGHI A, LI L. Joint asymmetric tolerance design and manufacturing decision-making for additive manufacturing processes [J]. IEEE Transactions on Automation Science and Engineering, 2018, 16(3): 1259-1270.

刘鹏, 洪军, 刘志刚, 等. 采用自适应遗传算法的机床公差分配研究 [J]. 西安交通大学学报, 2016, 50(1): 115-123.

LIU Peng, HONG Jun, LIU Zhigang, et al. Research on the tolerance allocation of machine tools based on adaptive genetic algorithm [J]. Journal of Xi'an Jiaotong University, 2016, 50(1): 115-123.

MOURA J, HUTCHISON D. Game theory for multi-access edge computing: survey, use cases, and future trends [J]. IEEE Communications Surveys Tutorials, 2018, 21(1): 260-288.

HALLMANN M, SCHLEICH B. From tolerance allocation to tolerance-cost optimization: a comprehensive literature review [J]. International Journal of Advanced Manufacturing Technology, 2020, 107(1): 1-54.

LU C, ZHAO W H, YU S J. Concurrent tolerance design for manufacture and assembly with a game theoretic approach [J]. International Journal of Advanced Manufacturing Technology, 2012, 62(1/2/3/4): 303-316.

刘永奎, 王力翚, 王曦, 等. 云制造再探讨 [J]. 中国机械工程, 2018, 29(18): 2226-2237.

LIU Yongkui, WANG Lihui, WANG Xi, et al. A revisit to cloud manufacturing [J]. China Mechanical Engineering, 2018, 29(18): 2226-2237.

WU Z, LIU T, GAO Z, et al. Tolerance design with multiple resource suppliers on cloud-manufacturing platform [J]. International Journal of Advanced Manufacturing Technology, 2016, 84(1/2/3/4): 335-346.

WANG T, LI C, YUAN Y, et al. An evolutionary game approach for manufacturing service allocation management in cloud manufacturing [J]. Computers Industrial Engineering, 2019, 133(7): 231-240.

LI S, LIU X, WANG Y, et al. A cubic quality loss function and its applications [J]. Quality and Reliability Engineering International, 2019, 35(4): 1161-1179.

HUANG Z, XIONG X, CHEN W, et al. Three bounded proofs for nonlinear multi-input multi-output approximate dynamic programming based on the Lyapunov stability theory [J]. Optimal Control Applications and Methods, 2018, 39(1): 35-50.

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